Real-time hand postures recognition using low computational complexity Artificial Neural Networks and Support Vector Machines

被引：6
作者：
Bragatto, Ticiano A. C. [1 ]
Ruas, Gabriel S. I. [1 ]
Lamar, Marcus V. [2 ]
机构：
[1] Univ Brasilia, Dept Elect Eng, Brasilia, DF, Brazil
[2] Univ Brasilia, Dept Comp Sci, Brasilia, DF, Brazil
来源：
2008 3RD INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS, CONTROL AND SIGNAL PROCESSING, VOLS 1-3 | 2008年
关键词：
hand posture recognition; Artificial Neural Networks; Support Vector Machines;
D O I：
10.1109/ISCCSP.2008.4537470
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes two main techniques for reduce computational complexity on Artificial Neural Networks, using piecewise linear activation function, and Support Vector Machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.
引用
收藏
页码：1530 / +
页数：2
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